scholarly journals Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1369
Author(s):  
Afiq Izzudin A. Rahim ◽  
Mohd Ismail Ibrahim ◽  
Kamarul Imran Musa ◽  
Sook-Ling Chua ◽  
Najib Majdi Yaacob
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Patient Satisfaction ◽  
Quality Of Care ◽  
Online Reviews ◽  
Care Quality ◽  
Learning Technologies ◽  
Public Hospitals ◽  
Patient Dissatisfaction

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study’s objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.

scholarly journals Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

2021 ◽  
Vol 18 (18) ◽  
pp. 9912
Author(s):  
Afiq Izzudin A. Rahim ◽  
Mohd Ismail Ibrahim ◽  
Kamarul Imran Musa ◽  
Sook-Ling Chua ◽  
Najib Majdi Yaacob
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Quality Of Care ◽  
Service Quality ◽  
Hospital Quality ◽  
Public Hospitals ◽  
Social Media Data ◽  
Positive Sentiment ◽  
Media Data

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

scholarly journals Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia

2021 ◽  
Vol 18 (14) ◽  
pp. 7454
Author(s):  
Afiq Izzudin A. Rahim ◽  
Mohd Ismail Ibrahim ◽  
Kamarul Imran Musa ◽  
Sook-Ling Chua
Keyword(s):  
Social Media ◽  
Patient Satisfaction ◽  
Online Reviews ◽  
Tertiary Hospital ◽  
Public Hospitals ◽  
Hospital Patient ◽  
Healthcare Organizations ◽  
Hospital Accreditation ◽  
Cross Sectional ◽  
The Impact

Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time.

scholarly journals Patient Satisfaction in a University Hospital Emergency Department in Turkey

2005 ◽  
Vol 48 (1) ◽  
pp. 59-62 ◽  
Author(s):  
Cuma Yildirim ◽  
Hasan Koçoğlu ◽  
Sıtkı Göksu ◽  
Nurullah Gunay ◽  
Haluk Savas
Keyword(s):  
Emergency Department ◽  
Patient Satisfaction ◽  
Quality Of Care ◽  
Quality Care ◽  
Waiting Times ◽  
Adult Patients ◽  
University Hospital ◽  
Patient Dissatisfaction ◽  
Insurance Carrier

Objective: Patient satisfaction, an indicator of the quality of care provided by emergency department (ED) personnel, is a significant issue for EDs. The purpose of this study was to identify factors associated with patient satisfaction and dissatisfaction, and to describe demographic characteristics of those surveyed in a university hospital ED. Methods: All adult patients who consecutively presented to the ED between 8:00 a.m. and 5:00 p.m. on weekdays were included in the study. Patients were asked to complete a questionnaire prior to discharge. The questionnaire asked about the attitude, politeness, and efficiency of the medical and ancillary staff, the reason for preferring our centre and reasons for dissatisfaction. Results: Two-hundred and forty-five adult patients presenting to our ED were included in this study. Forty-five percent of patients preferred our ED because of the previous perception of higher quality of care, informed by other people previously treated in this ED unit, and 35% because of restrictions by their health insurance carrier. The main causes of patient dissatisfaction were lengthy waiting times (27%). Conclusion: As a result, lengthy waiting time was the major reason for patient dissatisfaction, and high quality care together with insurance restrictions were the main reasons for preference of this university hospital ED.

scholarly journals Effective coverage and budget implications of skill-mix change to improve neonatal nursing care: an explorative simulation study in Kenya

2019 ◽  
Vol 4 (6) ◽  
pp. e001817 ◽  
Author(s):  
Apostolos Tsiachristas ◽  
David Gathara ◽  
Jalemba Aluvaala ◽  
Timothy Chege ◽  
Edwine Barasa ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Neonatal Mortality ◽  
Nursing Care ◽  
Public Hospitals ◽  
Sensitivity Analyses ◽  
Full Time ◽  
Skill Mix ◽  
Neonatal Nursing ◽  
Low Middle Income Countries

IntroductionNeonatal mortality is an urgent policy priority to improve global population health and reduce health inequality. As health systems in Kenya and elsewhere seek to tackle increased neonatal mortality by improving the quality of care, one option is to train and employ neonatal healthcare assistants (NHCAs) to support professional nurses by taking up low-skill tasks.MethodsMonte-Carlo simulation was performed to estimate the potential impact of introducing NHCAs in neonatal nursing care in four public hospitals in Nairobi on effectively treated newborns and staff costs over a period of 10 years. The simulation was informed by data from 3 workshops with >10 stakeholders each, hospital records and scientific literature. Two univariate sensitivity analyses were performed to further address uncertainty.ResultsStakeholders perceived that 49% of a nurse full-time equivalent could be safely delegated to NHCAs in standard care, 31% in intermediate care and 20% in intensive care. A skill-mix with nurses and NHCAs would require ~2.6 billionKenyan Shillings (KES) (US$26 million) to provide quality care to 58% of all newborns in need (ie, current level of coverage in Nairobi) over a period of 10 years. This skill-mix configuration would require ~6 billion KES (US$61 million) to provide quality of care to almost all newborns in need over 10 years.ConclusionChanging skill-mix in hospital care by introducing NHCAs may be an affordable way to reduce neonatal mortality in low/middle-income countries. This option should be considered in ongoing policy discussions and supported by further evidence.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

scholarly journals Humans rely more on algorithms than social influence as a task becomes more difficult

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Bogert ◽  
Aaron Schecter ◽  
Richard T. Watson
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Social Influence ◽  
Correct Answer ◽  
Online Reviews ◽  
Personal Networks ◽  
Human Judgment ◽  
Individual Decision ◽  
Individual Decision Making

AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

scholarly journals Need to Intervene: An Exploratory Study of Nurses’ Experiences With Patienthood

2021 ◽  
Vol 8 ◽  
pp. 237437352199884
Author(s):  
Marian A O Cohen ◽  
Jim McQuaid ◽  
Ruth Remington
Keyword(s):  
Patient Satisfaction ◽  
Quality Of Care ◽  
Healthcare System ◽  
Family Caregiver ◽  
Exploratory Study ◽  
Medical Personnel ◽  
Healthcare Personnel ◽  
Coordination Of Care ◽  
Professional Perspective

Much has been written about the patient experience, but there is little information about experiences of providers as patients. Since lay patients and providers have differing perspectives and expectations, it is important to identify those elements shared by those in each group and those that diverge. This study identified experiences of nurses as being a patient or a family caregiver of a patient as well as identified assessments of the healthcare system by nurses. An exploratory study using a self-administered electronic questionnaire with a group of registered nurses was conducted. Assessments of the system by responders were positive when addressing quality of care, interactions among healthcare personnel, and interactions with patients. However, when discussing their experiences as patient, nurses reported they encountered problems with coordination of care, responses of medical personnel, attention to details of care, and responses to their attempts to become more involved. Results confirm issues raised by patients who are not medical experts in patient satisfaction studies. Adding a professional perspective highlights where problems with the healthcare system lie.

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